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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum Sieving for Isotopic Separations of Gases Using Porous Materials─30 Years of Progress

Here, this paper reviews theoretical and experimental efforts to establish microporous materials that can be used to separate isotopologues of molecular gases such as H 2 and D 2 . Emphasis is placed on use of simplified models to highlight the quantum phenomena that make these separations possible. In equilibrium adsorption, differences in zero point energy in the adsorbed states of molecules favor binding of heaver isotopologues (e.g. D 2 relative to H 2 ). Experimental data showing this effect was reported as early as 1933, but the theoretical work of Beenakkers et al. in 1995 [Chem. Phys. Lett. 232 (1995) 379-382] spurred modern efforts to develop materials with strong so-called quantum sieving. In porous materials where the transition state for molecular diffusion is more strongly confined than the energy minima associated with equilibrium adsorption, differences in zero point energies between these two sites can lead to isotopic differences in molecular diffusivities. This effect favors diffusion of heavier species (e.g. D 2 ) relative to lighter species (e.g. H 2 ). This so-called kinetic quantum sieving has been observed experimentally in porous carbons and in porous organic cage materials. We show that quantum tunneling, which favors hopping of lighter species across energy barriers, diminishes the strength of kinetic quantum sieving but that it appears to make only a small contribution to the net molecular diffusivities in many porous materials of practical interest.

Sholl, David S. [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

Preliminary results of molten salt corrosion of high entropy alloys manufactured at INL

Materials development is needed to support the design and deployment of advanced nuclear reactors that will operate at higher temperatures and in harsher environments than the current light water reactor fleet. High entropy alloys have been identified as a class of alloy that could address the needs of advanced reactors, such as the molten salt reactors, due to their potential strength, radiation resistance, and corrosion resistance. This project summarizes the work done in the year 2024 for the Additive Manufacturing of High Entropy Alloys for Nuclear Applications Project. This part of the work has focused on molten salt corrosion of high entropy alloys which were developed and manufactured at INL.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Additively Manufactured Carbon Fiber and Fused Deposition Modeling PLA Interlaminar Shear Testing Report

This test report summarizes the torsional test setup, procedure, and results for interlaminar shear (ILS) failure of additively manufactured carbon fiber (CFAM) material developed at LLNL. In addition, the report also includes testing of fused deposition modeled (FDM) polylactic acid (PLA), including ILS test standard D7078 and the same torsional test mentioned above. The test hopes to characterize ILS properties of CFAM, however substantial further testing and improvements still need to be done to induce a proper failure mode. This testing was performed by Sundi Win as part of her summer internship project in Kinetic Technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

New systems in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) serves as a common library of classes between applications developed for advanced reactor analysis, fusion device engineering, spent fuel cask analysis, geochemistry studies, among other fields. These applications drive the development of the framework to meet their needs. Systems in MOOSE group capabilities that share a common purpose and generally common code. They can be leveraged by all downstream applications, providing extensive code re-use and shared maintenance. They facilitate the discovery by new users of the classes meeting at least partially their needs, and offer the same opportunities for customization as other systems. The addition of a new system to MOOSE opens new ways of solving or discretizing nonlinear problems, of performing distributed postprocessing, and a plethora of other needs. While new systems can be introduced in downstream applications rather than at the framework level, the framework team monitors common needs across the community and often triggers their addition. Documentation, training material, development needs can be centralized, limiting duplicated work across the community. The last three years have seen a large expansion in the capabilities of MOOSE. The supporting role of the framework in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has created numerous feature requests to support neutronics, thermal hydraulics, computational fluid dynamics and thermo-mechanics simulations in the Griffin, SAM, Pronghorn and Bison applications respectively. Similarly, laboratory-directed research and development (LDRD) projects in additive manufacturing, high-Reynolds flow simulations, structure optimization also necessitate an expansion of the framework capabilities. This summary reports on the new systems created in MOOSE, their design, their capabilities and some of the relevant interfaces.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Preliminary Study on Fine-Grained Power and Energy Measurements on Grace Hopper GH200 with Open-Source Performance Tools

The increasing adoption of tightly integrated, heterogeneous architectures, combined with the slowdown of Moore’s law, has made application power and energy-driven optimizations critical to efficiently use high-performance computing systems. This paper introduces a newly developed open-source toolkit that seamlessly integrates the Linux real-time hardware monitoring program hwmon with the Performance Application Programming Interface and the Score-P performance measurement system, thereby enabling fine-grained power and energy measurements for high-performance computing applications. Our primary target platform is the Wombat test bed, which is a system based on the NVIDIA GH200 superchip. The toolkit can capture transient power peaks with high temporal resolution (50 ms) and, thanks to Score-P integration, can map power metrics to specific code regions, thereby providing actionable information on power-intensive operations and inefficiencies. The toolkit also provides a holistic view of both the power and the energy consumption of the entire GH200 superchip by covering all major components: the Grace CPU, the Hopper GPU, and the I/O subsystem. Experiments that use Locally Self-consistent Multiple Scattering, which is an application for first-principles calculations of materials developed at Oak Ridge National Laboratory, have demonstrated the tool’s ability to identify transient power spikes and uncover opportunities for energy-aware optimizations. Additionally, we introduce a Python-based utility for converting Open Trace Format 2 traces to Parquet format, thus enabling advanced data analysis for numerical integration methods applied to power data for accurate energy profiling.

Hernandez Mendoza, Oscar [ORNL] (ORCID:00000002538↗
DOE OSTI2025

Elucidation of Local Ordering and Atomic-Scale Structure in Polymer-Derived SiOC

Silicon oxycarbide (SiOC) is a versatile ceramic material with tunable microstructure and compositions that can be modulated through precursor chemistry and processing conditions. Though there are several noteworthy uses of SiOC across a range of application spaces, the difficulties in elucidating the short- to medium-range order within these materials have limited the maturation of strategies to precisely control SiC x O 4–x compositions for user-tailored applications. In this contribution, we implement a range of synchrotron scattering and spectroscopy methods coupled with stochastic modeling techniques to elucidate changes in local chemistry and structure associated with the pyrolysis of a commercially available SiOC polymer precursor. Stochastic modeling approaches provide valuable insights into decoupling local Si–O and Si–C environments while confirming predominate heterogeneous phases in materials. Using pyrolysis temperatures between 250 to 800 °C results in a heterogeneous material predominately composed of SiOC and amorphous SiO 2 domains. At 1100 °C, redistribution of Si–C pairs in the SiOC network and Si–O from the SiO 2 domains create a more ordered SiOC phase with local cubic SiC-like ordering. In addition, residual carbon leads to a detectable carbon phases at 1100 °C that persist at higher temperatures. These efforts address the difficulties of obtaining atomic-scale insights into the local structure and nanoscale heterogeneities in SiOC, providing pathways toward establishing structure–property relationships for future materials development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Assessment of preliminary results of baseline high entropy alloys manufactured at INL

Materials development is needed to support the design and deployment of advanced nuclear reactors that will operate at higher temperatures and in harsher environments than the current light water reactor fleet. High entropy alloys have been identified as a class of alloy that could address the needs of advanced reactors, such as the molten salt reactors, due to their potential strength, radiation resistance, and corrosion resistance. This project summarizes the work done in fiscal year 2024 for the Additive Manufacturing of High Entropy Alloys for Nuclear Applications Project. This work has focused on examination of potential alloy compositions, down selection based on simulated results, and preliminary testing of down selected alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Post-Irradiation Examination to Quantify Irradiation-Induced Bowing of SiGA® Silicon Carbide Composite Structures (Final CRADA Report – NFE-23-09937)

As part of the SiC-based material development at General Atomics Electromagnetic Systems (GA-EMS), this project involved the first-of-a-kind experimental post-irradiation examination of the irradiation-induced bowing response in SiC composite structures under a neutron flux gradient. The SiC composite miniature channel specimen was provided by GA-EMS for the irradiation experiment. To quantify the irradiation-induced bowing of the channel specimen, a series of visual and dimensional inspections were conducted using the unique capabilities at Oak Ridge National Laboratory (ORNL), including a custom profilometry rig. The post-irradiation examination results of this project will assist GA-EMS in validating their fuel performance model for SiC-based core structures in neutron irradiation environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

VISIONARY: Virtual Intelligence System for Optimizing Novel Analytical Research Yields

VISIONARY is an AI system that accelerates energy materials discovery by automatically generating hypotheses about structure-property relationships. It analyzes patterns in materials data, identifies promising correlations, and proposes testable scientific hypotheses without human intervention. By streamlining this reasoning process, VISIONARY helps researchers efficiently identify candidate materials with desired properties, significantly speeding up the materials development pipeline for energy applications. During the project, we developed a standalone application. The application uses a combination of papers provided by the user and data collected from FutureHouse’s dataset to build an understanding of the background that the user wants to explore for the hypothesis.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Shaping the FutureWorkforce: Challenges and Lessons Learned in HPC Education from National Labs and Computing Centers

Workforce training at national laboratories and computing centers is essential and typically falls into two categories: foundational training for newcomers and advanced training for experienced users. Foundational topics—such as version control, build systems, and basic HPC usage—are largely transferable across institutions, while cluster-specific training varies due to differences in hardware, job schedulers, and local workflows. Training on emerging technologies is split between hardware-specific content and broadly applicable programming paradigms. Here, to reduce redundancy and increase impact, national labs, computing centers, and vendors are collaborating through initiatives like the HPC Training Working Group to share best practices, co-develop materials, and broaden outreach. These coordinated efforts aim to make HPC training more accessible, scalable, and consistent across the community.

HPC↗
DOE OSTI2026

Green Methanol via an Integrated Direct Air Capture, CO 2 Electrolyzer, and Hydrogenation Reactor

This project pioneered a groundbreaking reactor design to produce green methanol by harnessing the electrochemical CO 2 reduction reaction (eCO 2 RR), a cornerstone of power-to-fuels technology. The effort integrated three innovative technologies to achieve carbon-neutral methanol production at a target cost of under $\$$800/ton: 1. Direct Air Capture (DAC): Using a cutting-edge sorbent material developed at Holocene, scalable models were developed to integrate captured atmospheric CO₂ into the reactor system. 2. Intermediate-Temperature CO 2 Electrolyzer: Developed by the University of Tennessee (UTK), this electrolyzer utilizes a cost-effective, proton-conducting solid acid electrolyte (CsH 2 PO 4 , CDP) and a mixed-metal oxide cathode. It achieves high faradaic efficiencies (>98%) by effectively suppressing hydrogen evolution at high current densities, converting CO 2 to CO with remarkable selectivity. 3. Catalysis and Reactor Engineering: Oak Ridge National Laboratory (ORNL) contributed world-class expertise in heterogeneous catalysis and reactor design. Their advanced ASPEN modeling drove systems integration and supported techno-economic and life cycle analyses. This effort was further bolstered by partnerships with industry leaders Air Company and Plug Power, who provided critical guidance on scaling, systems engineering, and the integration of water electrolyzers into large-scale operations. During Phase 1, the team focused on modeling and validating a lab-scale reactor demonstrating the feasibility of the integrated approach. Key accomplishments include a 52% increase in current density at 0.8 V while maintaining >98% CO faradaic efficiency, successful 10× scale-up of the electrolyzer with performance within 5% of coin-cell results, best-in-class durability (168-hour test at 0.6 V with 0.14 mA/cm 2 -h degradation), validated TEA confirming the $\$$800/ton methanol target, and completed preliminary LCA showing potential for net-negative GHG emissions under renewable energy scenarios.

10 SYNTHETIC FUELS↗
DOE OSTI2026

Thermokinetic mixing compounding for polymer composites – a comprehensive review

High-speed thermokinetic mixers (K-mixers) represent an advanced compounding technology that employs intense shear and friction to convert kinetic energy directly into thermal energy. This mechanism enables rapid mixing cycles, often under one minute, facilitating exceptional filler dispersion while minimizing the material’s thermal history. This is particularly effective for compounding challenging materials, including heat-sensitive biopolymers, wet filler feedstocks, and nanofillers prone to agglomeration. As the first comprehensive review of this technology, this article synthesizes the fundamental principles of thermokinetic mixing (K-mixing) and surveys recent advances in polymer composite fabrication. We contrast the working principles of K-mixers with conventional twin-screw extrusion, highlighting distinct advantages in dispersing nanoscale fillers, exfoliating layered materials, and processing wet cellulosic feedstocks and ultra-high filler loadings (e.g., 85 wt%). Furthermore, strategies to optimize filler–matrix interfacial bonding under rapid-processing constraints, such as the kinetic selection of compatibilizers and fiber surface treatments, are evaluated. Finally, we analyze key structure-processing-property relationships and outline future directions in scaling up, reactive processing, and hybrid material development.

Zhang, Xuefeng [University of Maine]↗
DOE OSTI2025

Rheology of lignin and lignin-based solutions, dispersions, gels, polymer blends, and melts

Lignin is an abundant resource that finds application in energy and sustainable materials development. In addition to the utilization of lignin in three-dimensional (3D) printing and hydrogel production, recent studies have reported the use of lignin as a liquid fuel additive. The characterization of the rheological properties of lignin and its derivatives is a dynamic and evolving field, underpinned by advances in experimental, analytical, and modeling techniques. Here, this review provides a comprehensive overview of the recent progress in the study of lignin rheology, highlighting the interplay between structure, modification, and flow behavior in lignin-based solutions, dispersions, gels, polymer blends, and melts. A specific highlight of this review is how lignin concentration affects the rheological properties of lignin-based solutions and dispersions. Furthermore, the effect of lignin type on the properties of 3D-printed lignin-based composites is discussed. For polymer systems, this review discussed lignin-in-polymer solutions separately from lignin-filled polymer systems. Finally, challenges and perspectives on lignin rheology are presented.

Dispersions↗
DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗
DOE OSTI2024

In the Mix : A Workshop Merging Computational Chemistry and Electrochemistry Alongside Data Science

As chemistry expands to more complex and interdisciplinary areas, a new generation of diverse researchers must engage with science and learn effective cross-disciplinary collaboration and communication. To these ends, we designed and implemented In the Mix, a graduate student-led, two-day workshop for undergraduate students promoting collaborative science in the context of energy storage innovations. Here, the interactive workshop was designed for future and emerging researchers to gain hands-on experience with data science, computational chemistry, and electrochemistry techniques that are critical for developing materials for battery technologies. Participants also visited commercial renewable energy facilities to help them connect discovery-based research with industry and broader societal considerations. The workshop content and structure ensured that participants experienced the interrelatedness of the fields and understood the importance of collaborative research to yield scientific advances with real-world applications. An external team evaluated the workshop and participants’ perceptions of their experiences. While our research context was energy storage, the workshop goals and outcomes are applicable to other contexts. Interdisciplinary, experiential workshops are a key avenue to broadening participation in science and research, and the ideas presented here can be readily modified for other scientific contexts and/or incorporated as broader impact activities.

25 ENERGY STORAGE↗
DOE OSTI2024

Analytic Gradients for Equation-of-Motion Coupled Cluster with Single, Double, and Perturbative Triple Excitations

Understanding the process of molecular photoexcitation is crucial in various fields, including drug development, materials science, photovoltaics, and more. The electronic vertical excitation energy is a critical property, for example in determining the singlet-triplet gap of chromophores. However, a full understanding of excited-state processes requires additional explorations of the excited-state potential energy surface and electronic properties, which is greatly aided by the availability of analytic energy gradients. Owing to its robust high accuracy over a wide range of chemical problems, equation-of-motion coupled-cluster with single and double excitations (EOM-CCSD) is a powerful method for predicting excited state properties, and the implementation of analytic gradients of many EOM-CCSD (excitation energies, ionization potentials, electron attachment energies, etc.) along with numerous successful applications high- lights the flexibility of the method. In specific cases where a higher level of accuracy is needed or in more complex electronic structures, the inclusion of triple excitations becomes essential, for example, in the EOM-CCSD* approach of Saeh and Stanton. In this work, we derive and implement for the first time the analytic gradients of EOMEE-CCSD*, which also provides a template for analytic gradients of related ex- cited state methods with perturbative triple excitations. Here, the capabilities of analytic EOMEE-CCSD* gradients are illustrated by several representative examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deuteration Effects on the Physical and Optoelectronic Properties of Donor–Acceptor Conjugated Polymers

The significant differences in scattering cross sections between deuterium and protium are unique to neutron scattering techniques and have been a long-standing area of interest within the neutron scattering community. Researchers have explored selective deuteration to manipulate scattering contrast in soft matter systems, leading to the widespread use of deuterium labeling in materials development. As deuteration changes the atomic mass, it alters physical properties such as molecular volume, polarizability, and polarity, which in turn may affect noncovalent interactions and crystal ordering. Despite previous studies, there remains a limited understanding of how deuteration impacts donor–acceptor (DA) conjugated polymers. To address this, we synthesized deuterated DPP polymers and systematically investigated the effects of side-chain deuteration on their thermal stability, crystal packing, morphology, and optoelectronic properties. We found that deuteration increased the melting and crystallization temperatures of DPP polymers, although it did not significantly alter their morphology, molecular packing, or charge mobility. These properties were assessed by using atomic force microscopy (AFM), X-ray scattering, and thin-film transistor device measurements, respectively, for DPP polymers. Our work shows that deuterium labeling could be a powerful method for controlling scattering length density, enabling neutrons to study the structure and dynamics of conjugated polymers without impacting their electronic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗